As memory and diagnostic capacity in implantable medical devices, such as implantable cardioverter-defibrillators (ICDs), for example, increase, the amount of time required to adequately review the available data associated to determine whether the detection of episodes and delivery of therapy by the device was appropriate also increases. Since the number of identified ICD indications continues to increase, while the amount of time that is available for post-process review of data decreases, the classification of ICD episodes requires significant levels of expertise. As a result, the number of clinicians having the required expertise has been reduced, which could result in a reduction in the quality of management of those patients having implanted devices. Therefore, an algorithm that post-processes and automatically reviews each previously detected episode upon interrogation could address these concerns by accurately classifying episodes and potentially suggesting ICD parameter changes and/or medical therapy, such as changes in medication, therapy delivery, use of ablation procedures, etc.
Reviewing the data stored in the ICD memory at clinic follow-up requires expert knowledge to discriminate between true ventricular arrhythmias and unnecessary detection of non-ventricular arrhythmias. As the ICD population increases, the time needed to review all ICD detected episodes with careful detail also increases. Automatically identifying ICD stored events that may have been inappropriately detected as episodes by the device may decrease the time required to review episodes and to assure that unnecessary detections are properly reviewed.
Therefore, an algorithm that correctly classifies each detected episode during post-processing review of data stored in an implantable device is needed in order to reduce the clinician time to review episodes, and to give the clinician confidence that each incorrect ICD detection was brought to their attention.